Preference Transformer: Modeling Human Preferences using Transformers for RL

• URL: http://arxiv.org/abs/2303.00957v1
• Date: Thu, 2 Mar 2023 04:24:29 GMT
• Title: Preference Transformer: Modeling Human Preferences using Transformers for RL
• Authors: Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee
• Abstract summary: Preference Transformer is a neural architecture that models human preferences using transformers. We show that Preference Transformer can solve a variety of control tasks using real human preferences, while prior approaches fail to work.
• Score: 165.33887165572128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging to scale since it requires a large amount of human feedback to learn a reward function aligned with human intent. In this paper, we present Preference Transformer, a neural architecture that models human preferences using transformers. Unlike prior approaches assuming human judgment is based on the Markovian rewards which contribute to the decision equally, we introduce a new preference model based on the weighted sum of non-Markovian rewards. We then design the proposed preference model using a transformer architecture that stacks causal and bidirectional self-attention layers. We demonstrate that Preference Transformer can solve a variety of control tasks using real human preferences, while prior approaches fail to work. We also show that Preference Transformer can induce a well-specified reward and attend to critical events in the trajectory by automatically capturing the temporal dependencies in human decision-making. Code is available on the project website: https://sites.google.com/view/preference-transformer.

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